Adaptive real-time driver advisory control for a hybrid electric vehicle to achieve fuel economy

ABSTRACT

A vehicle powertrain controller includes a fuzzy logic-based adaptive algorithm with a learning capability that estimates a driver&#39;s long term driving preferences. An adaptive algorithm arbitrates competing requirements for good fuel economy, avoidance of intrusiveness and vehicle drivability. A driver&#39;s acceptance or rejection of advisory information may be used to adapt subsequent advisory information to the driving style. Vehicle performance is maintained in accordance with a driver&#39;s driving style.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a division of U.S. application Ser. No. 12/796,038filed Jun. 8, 2010, the disclosure of which is incorporated in itsentirety by reference herein.

BACKGROUND

A hybrid electric vehicle relies upon two power sources for deliveringpower to vehicle traction wheels. One power source typically is aninternal combustion engine and the other power source is a battery and amotor, together with a generator. In a so-called power-split hybridelectric vehicle powertrain, a generator is mechanically coupled to theengine and is electrically coupled to the battery and the motor. Forexample, in U.S. Pat. No. 7,467,033, a split power delivery path isestablished by a simple planetary gear unit whereby the generator isconnected drivably to the sun gear of the planetary gear unit and theengine is connected to the carrier of the planetary gear unit. The ringgear of the planetary gear unit is mechanically connected to the motor.Although a split power hybrid electric vehicle powertrain is capable ofembodying the present invention, other types of hybrid electric vehiclepowertrain architectures may embody the invention as well, includingnon-hybrid powertrains in which an internal combustion engine is a powersource.

Although known hybrid electric vehicle powertrains provide a significantimprovement in overall powertrain fuel economy and reduce undesirableexhaust gas emissions compared to conventional powertrains, there is apotential for still further improvement in fuel economy by makingadjustments in factors such as driver style, driver behavior and driverpreferences. This may be done by providing appropriate feedback to thedriver with regard to adjustments that affect fuel economy. The feedbackmay be in the form of visual indicators or displays.

A hybrid powertrain typically includes a vehicle control system thatcoordinates power distribution from each power source to achieve anoptimum balancing of torque, speed and power from each power source. Thecontrol system includes an engine controller, a transmission controller,a high voltage battery controller, a regenerative braking system, and ahigh voltage battery. A vehicle system controller performs an overallvehicle system coordination and oversight by communicating with severalsubsystem controllers. The vehicle system controller manages andcoordinates the driveline functions to satisfy the driver's torquerequest and to balance energy flow to and from the subsystems. Areal-time advisory system can provide direct advice to drivers regardingoptimal accelerator pedal and brake pedal inputs to the vehicle systemcontroller.

SUMMARY

The invention includes a real-time driver advisory system using a fuzzylogic-based adaptive algorithm with a learning strategy that estimates adriver's long-term and short-term driving preferences. The algorithm isused to provide a significant advancement in the capability of knownnon-adaptive real-time fuel economy advisory systems, which includevisual and haptic feedback information to the driver so that the drivercan change driving style or behavior for a given vehicle condition toimprove fuel economy. Applicants' algorithm is tuned to maximize fueleconomy without significantly affecting performance of the vehicle andwithout being intrusive for the driver. The algorithm learns driverintentions by monitoring driving style and driver behavior, and itaddresses the issue of intrusiveness due to the advisory feedback. Thisbalances the competing requirements for improved fuel economy anddrivability by maintaining vehicle performance acceptable to a driver'slearned driving style and behavior while providing a mechanism forimproving fuel economy.

A driver request for power at the fraction wheels is conveyed by theadvisory system to the controller by monitoring an accelerator pedal anda brake pedal. The request level and profile can affect fuel economy inan electric vehicle in which there is more than one energy source. Basedon driver traction torque requests, different operating modes of ahybrid electric vehicle can be selected. That selection may or may notbe optimal for achieving optimal fuel efficiency.

The controller of the present invention uses input variables, outputvariables and associated fuzzy rule sets to make certain that thedriver's selection of an operating mode will be optimal for achievingbest fuel efficiency. The controller takes into account drivability andfuel consumption, together with an essence of prediction, and takesadvantage of opportunistic conditions.

The adaptive algorithm of the invention is capable of improving driverbehavior and driving style without being perceived as ineffective orintrusive while achieving fuel economy improvement.

To identify fuel consumption and vehicle driving state, the powertraininputs used are normalized fuel consumption (ƒc_(n)) and vehicle speed(vs). To address the criteria for acceptable drivability or performance(the vehicle should be able to achieve minimum acceptable accelerationat all times), one of the other inputs that is used is the normalizedvehicle acceleration (a_(n)). To predict the driver's behavior and makeuse of opportunistic states of the driving behavior, the driver pedalresponse (ζ_(ΔA)), is used as the final input.

The outputs of the controller are the advised change (delta) of theaccelerator pedal position (ΔA_(u) _(—) _(lim)) together with two otheroutputs; i.e., a maximum integrator offset (I_(oft) _(—) _(max)) and aminimum integrator offset (I_(oft) _(—) _(min)). This advised change ofaccelerator pedal position is fed into an integrator to obtain anadvised accelerator pedal threshold (A_(th)), which is limited to lowerand upper bounds. These lower and upper bounds are calculated based onactual pedal position, minimum integral offset, and maximum integraloffset. The advised accelerator pedal threshold, A_(th), is used as athreshold to compare the advised accelerator pedal position to actualaccelerator pedal position, A_(pp), such that if A_(pp) is greater thanA_(th), then a feedback signal (haptic or visual) can be sent to thedriver indicating that a condition exists where fuel economy of thevehicle can be improved by decreasing the acceleration pedal position,A_(pp).

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a schematic representation of a power-split hybrid electricvehicle powertrain capable of embodying the invention.

FIG. 2 is a schematic block diagram of a fuzzy advisory controller forthe powertrain of FIG. 1.

FIG. 3 is a diagram of membership functions for the fuzzy controller ofFIG. 2 wherein the membership functions are characterized as being in“accept” and “reject” categories.

FIG. 4 is a schematic block diagram of an adaptive real-time advisorycontrol system showing an adaptive algorithm used to convert a driverbehavior effect as an input to a fuzzy advisory controller.

DETAILED DESCRIPTION

As required, detailed embodiments of the present invention are disclosedherein; however, it is to be understood that the disclosed embodimentsare merely exemplary of the invention that may be embodied in variousand alternative forms. The figures are not necessarily to scale; somefeatures may be exaggerated or minimized to show details of particularcomponents. Therefore, specific structural and functional detailsdisclosed herein are not to be interpreted as limiting, but merely as arepresentative basis for teaching one skilled in the art to variouslyemploy the present invention.

FIG. 1 shows in schematic form a power-split hybrid electric vehiclepowertrain that is capable of embodying the improved control system ofthe invention. FIG. 1 shows a transaxle 10, which includes two electricmachines, a generator 12 and a motor 14, together with an engine 16. Aplanetary gear unit 18 provides a mechanical split power delivery pathfrom the engine to torque transfer gearing, which delivers drivingtorque to an axle and differential assembly for vehicle traction wheels,as shown at 22. A coordinated vehicle control system for managing thepower distribution from each of the power sources to the traction wheelsis provided. This coordination requires control algorithms to performthe balancing of torque speed and power from the two power sources.

The control system includes an controller for engine 16, a transmissioncontrol module (TCM), a high voltage battery and battery control module(BCM) and a regenerative braking system (BSCM) to control the engine,the transaxle, the battery and a regenerative braking subsystem,respectively. A vehicle system controller (VSC) performs an overallvehicle assessment and coordinated control by communicating with thesubsystem controllers. It manages and coordinates the powertrainfunctions to satisfy a driver's power request, and balances the energyflow to and from the multiple subsystems. The present invention will usea driver behavior variable since that variable plays a significant rolein achieving maximum vehicle fuel economy and reducing vehicle exhaustemissions. A real-time advisory system, using the present invention, canprovide direct advice to drivers regarding the optimal operation of theaccelerator pedal and the brake pedal inputs to help the driver improvefuel economy without using commands that are intrusive.

The fuzzy advisory controller is a main component of the adaptiveadvisory system. It uses a set of rules with fuzzy predicates and anapproximate reasoning method, as seen in Table 1 in the subsequentdescription, to summarize essentially a strategy that accounts forinstantaneous fuel consumption, vehicle speed, vehicle acceleration, andthe driver's torque request, in order to determine the upper bound ofthe torque request that accounts for maximum fuel efficiency anddrivability. The controller-calculated torque upper bound profile is adynamic threshold for the accelerator pedal position, which is used asan advisory warning for fuel economy cautious drivers. Its inputvariables consist of normalized (scaled) values of the fuel consumption(ƒ_(cn) or x₁), vehicle speed (v_(s) or x₂), vehicle acceleration (a_(n)or x₃), and driver accelerator pedal response pedal (ΔA_(pp) or x₄).

The main output of the controller is the advised change of the pedalposition (ΔA_(u) _(—) _(lim) or y₁). This advised change of acceleratorpedal position is integrated to obtain an advised accelerator pedalthreshold position (A_(th)) that is further compared to the actualaccelerator pedal position A_(pp). In the cases when A_(pp) is greaterthan A_(th), a feedback (using haptic or visual human-machine interfaceHMI) is sent to the driver indicating that a condition exists where fueleconomy of the vehicle can be improved by decreasing the acceleratorpedal position, App; i.e., by reducing the instantaneous torque request.

In addition to the main output of the advised change of the pedalposition, y₁, the fuzzy advisory controller includes two extra outputsthat are not common in conventional fuzzy logic controllers. Theseadditional outputs, seen in FIG. 2, are the maximum integrator offset 50(I_(oft) _(—) _(max) or y₂) and the minimum integrator offset 53(I_(oft) _(—) _(min) or y₃). The two additional outputs dynamicallyadjust the lower (A_(pp)+y₃) and upper (A_(pp)+y₂) bounds of the advisedaccelerator pedal threshold position to the current operating point asdefined by the actual pedal position. The purpose of these adjustablebounds is to avoid the integral windup and associated effects ofsaturation, consequent overshoots and undesirable transients. FIG. 2shows this fuzzy advisory controller. This fuzzy controller utilizes thehuman expert control knowledge and experience in vehicle driving tointuitively construct a strategy that emulates a desired drivingbehavior.

In order to introduce the rule-base model describing the input-outputrelationships in the controller, the input and output variables arepartitioned into fuzzy subsets. The input variables of the controllerare partitioned into two fuzzy subsets each (low or F_(i,L)) and high orF_(i,H)), i=[1,4]) in order to better describe the input-output logicalrelationship. The input fuzzy subsets are formalized by trapezoidalfunctions. The integrator offset output variables are partitioned intothree fuzzy subsets—high or h_(2H), low or h_(2L), and zero or h_(2Z)for the minimum integrator offset while the advised change ofaccelerator pedal position is quantified into four subsets: high orh_(1H), low or h_(1L), negative low or h_(1L), and negative high or−h_(1H). All output fuzzy subsets are fuzzy singletons. The partitioningof the input-output variables decomposes the space of the main factorsthat affect vehicle fuel economy and performance (instantaneous fuelconsumption, acceleration, speed, and accelerator pedal position) and isthe foundation of a rule-based model of the advisory strategy.

TABLE 1 Rule-base of the advanced MIMO fuzzy advisory controllerAntecedents Consequents If If If Then Then Then Rule ζ 

 A vs a_(n) If fc_(n) ΔA _(u) _lim I_(oft)_max I_(oft)_min No. is is isis is is is Conditions 1 Low Low Low Low High High Zero Steady stateefficient driving condition 2 Low Low Low High Low Low Zero Steady stateopportunistic condition where fuel consumption is high, so indicate thatdriving behavior is fine and prepare outputs to look for opportunity toimprove efficiency 3 Low Low High Low Low High Zero Almost steady statecondition where vehicle acceleration is high, but fuel consumption low,so indicate to driver that driving style is fine. 4 Low Low High High-Low Zero -Low Almost steady state condition where vehicle acceleration& fuel consumption is high, so take slow corrective action by indicating(after some delay) to driver that he/she needs to slow down. 5 Low HighLow Low High High Zero Steady state efficient driving condition. 6 LowHigh Low High Low Low Zero Steady state opportunistic condition wherefuel consumption is high, so indicate that driving style is fine andprepare to look for opportunity to improve efficiency 7 Low High HighLow Low High Zero Almost steady state condition where vehicleacceleration is high, but fuel consumption low, so indicate to driverthat driving style is fine. 8 Low High High High -High Zero -High Almoststeady state condition where vehicle acceleration & fuel consumption ishigh, so take fast corrective action by indicating to driver that he/sheneeds to slow down. 9 High Low Low Low -Low High Zero Transientpredictive condition where current conditions are fine, but due to thefaster driver response, use predictive actions to prepare for anyupcoming inefficiency. 10 High Low Low High -Low Low Zero Transientpredictive condition where current conditions are not efficient, soslowly prepare to indicate to driver to decrease pedal after transientevent is over. 11 High Low High Low High Low Zero Transient efficientdriving condition. 12 High Low High High -High Low Zero Transientpredictive condition where current conditions are not efficient, andquickly prepare to indicate to driver to decrease pedal after transientevent is over. 13 High High Low Low -Low Low Zero Transient predictivecondition where current conditions are not efficient, so slowly prepareto indicate to driver to decrease pedal after transient event is over.14 High High Low High -High Low Zero Transient opportunistic conditionwhere current conditions are not efficient, and quickly prepare toindicate to driver to decrease pedal after transient event is over. 15High High High Low Low Low Zero Transient condition where currentconditions are almost efficient 16 High High High High -Low Zero -LowTransient condition where current conditions are not efficient, soslowly (after some delay) indicate to driver to decrease pedal.

Vector y=[y₁ y₂ y₃] of deterministic values of the three outputs of theadvisory fuzzy controller is inferred by applying the simplified methodof reasoning on the set of 16 rules in Table 1:

$\begin{matrix}{y = \frac{\sum\limits_{j}{{F_{1,j_{1}}( x_{1} )}{F_{2,j_{2}}( x_{2} )}{F_{3,j_{3}}( x_{3} )}{{F_{4,j_{4}}( x_{4} )}\lbrack {h_{1,k_{1}}h_{2,k_{2}}h_{3,k_{3}}} \rbrack}}}{\sum\limits_{j}{{F_{1,j_{1}}( x_{2} )}{F_{2,j_{2}}( x_{2} )}{F_{3,j_{3}}( x_{3} )}{F_{4,j_{4}}( x_{4} )}}}} & (1)\end{matrix}$

where j_(s)={Low, High} for s=[1,4], k₁={High, Low, −High, −Low},k₂={High, Low, Zero}, and k₃={Zero, −Low, −High} represents the vectorof the three outputs and F_(i,j) ₁ (x_(i)) is the degree of firing(matching) the antecedent F_(i,j) ₁ membership function by thenormalized input x_(i). The output of the complete fuzzy controllersystem after the integration of the output y₁ can be described by thefollowing equation:

$\begin{matrix}{{u(n)} = \{ \begin{matrix}{{A_{pp} + y_{2}},} & {{{if}\mspace{14mu} u_{tmp}} > ( {A_{pp} + y_{2}} )} \\{{u_{tmp} = {\int_{0}^{t}{{y_{1}(\tau)}{\tau}}}},} & {{{if}\mspace{14mu} ( {A_{pp} - y_{3}} )} \leq u_{tmp} \leq ( {A_{pp} + y_{2}} )} \\{{A_{pp} - y_{3}},} & {{{if}\mspace{14mu} u_{tmp}} < ( {A_{pp} - y_{3}} )}\end{matrix} } & (2) \\{{where},{0 \leq {A_{pp} + y_{2}} \leq {A_{pp} - y_{3}} \leq 1}} & (3)\end{matrix}$

Substituting the values of y₁, y₂ and y₃ into equation (2) results inthe following:

$\begin{matrix}{A_{th} = \{ \begin{matrix}{{A_{pp} + \frac{\sum\limits_{j}{{F_{1,j_{1}}( x_{1} )}{F_{2,j_{2}}( x_{2} )}{F_{3,j_{3}}( x_{3} )}{F_{4,j_{4}}( x_{4} )}h_{2,k_{2}}}}{\sum\limits_{j}{{F_{1,j_{1}}( x_{1} )}{F_{2,j_{2}}( x_{2} )}{F_{3,j_{3}}( x_{3} )}{F_{4,j_{4}}( x_{4} )}}}},} & {{{if}\mspace{14mu} u_{tmp}} > ( {A_{pp} + y_{2}} )} \\{{u_{tmp} = {\int{\frac{\sum\limits_{j}{{F_{1,j_{1}}( x_{1} )}{F_{2,j_{2}}( x_{2} )}{F_{3,j_{3}}( x_{3} )}{F_{4,j_{4}}( x_{4} )}h_{1,k_{1}}}}{\sum\limits_{j}{{F_{1,j_{1}}( x_{1} )}{F_{2,j_{2}}( x_{2} )}{F_{3,j_{3}}( x_{3} )}{F_{4,j_{4}}( x_{4} )}}}{t}}}},} & {{{if}\mspace{14mu} ( {A_{pp} - y_{3}} )} \leq u_{tmp} \leq ( {A_{pp} + y_{2}} )} \\{{A_{pp} + \frac{\sum\limits_{j}{{F_{1,j_{1}}( x_{1} )}{F_{2,j_{2}}( x_{2} )}{F_{3,j_{3}}( x_{3} )}{F_{4,j_{4}}( x_{4} )}h_{3,k_{3}}}}{\sum\limits_{j}{{F_{1,j_{1}}( x_{1} )}{F_{2,j_{2}}( x_{2} )}{F_{3,j_{3}}( x_{3} )}{F_{4,j_{4}}( x_{4} )}}}},} & {{{if}\mspace{14mu} u_{tmp}} < ( {A_{pp} - y_{3}} )}\end{matrix} } & (4)\end{matrix}$

where A_(th) is the advised accelerator pedal position threshold, whichif followed, will result in fuel economy improvement as defined by therule-base in Table 1. This advice can be provided through variousmechanisms, such as a haptic mechanism where a vibrating motor ismounted on the accelerator pedal; or a visual mechanism where theinstrument cluster displays advice through a visual HMI (human-machineinterface).

The fuzzy partitioning and the rule-base are used to formalize theexpert defined strategy advising for fuel efficient driving. Theapproximate reasoning mechanism (1)-(4) transforms the rule-base into anonlinear multiple-input, multiple-output (MIMO) nonlinear mapping ofthe advisory controller.

The real time advisory controller is a fuzzy rule based controller thatuses key driver and vehicle related inputs together with a fuzzy rulesbase to provide feedback to the driver so fuel economy of the vehiclecan be improved.

While this real-time advisory controller with its rules base maximizesfuel economy without significantly affecting impacting the performanceof the vehicle, drivers with different types of driver styles, such assoft, normal or aggressive, may perceive the feedback differently. Forexample, on one extreme, some drivers may find this feedback to beintrusive based on their driving styles. Even with the potential forincreasing fuel economy, this feedback or advice, which results inreduced performance, can be unacceptable to such drivers. On the otherhand, another group of drivers may consider this feedback to well fit totheir driving style and preferences for fuel economy vs. performance.Hence an adaptive algorithm that can learn driver intentions bymonitoring their driving style and behavior can be used to address theseissues. Therefore, the objective of this algorithm is to use theestimated driver characterization to dynamically adapt the parameters ofthe fuzzy advisory controller (Table 1) to the specific driver andimprove its effectiveness.

The adaptive algorithm continually monitors the reaction of the driverto the recommendation of the advisory system (haptic signal) andestimates driver preferences for performance vs. fuel economy. The mainidea behind the adaptive advisory algorithm is that if the feedbackmechanism is not of driver's desire, his or her accelerator response orbehavior will indicate this fact.

From the perspective of the advisory feedback algorithm, two categoriesof a driving style can be defined. The first category is characterized,in general, with acceptance of the recommendations of the advisorysystem. Since the advisory algorithm is designed to improve fueleconomy, this category can be associated with “fuel efficiency awarestyle” of driving. The second category represents driving style that,most of the time, rejects or ignores the recommendations of the advisorysystem. Similarly, this category can be associated with “performanceoriented style” of driving. The driving style is characterized ratherthan the driver, assuming that specific goals, driving conditions, load,driver's cognitive/emotional state, etc., may cause a certain driver toexhibit different driving styles under different circumstances.

Numerically, the acceptance or rejection of the advisory systemrecommendations can be quantified through the difference(dA=A_(th)−A_(pp)) between the advised and the actual pedal position.Since the evaluation of the driving style with respect to therecommendations of the advisory system is not well defined and issubjective, fuzzy subsets are used to quantify the two categories ofdriving styles. Those categories are evaluated using probabilities basedon the specific driver actions. The fuzzy subsets associated with thecategories of acceptance/rejection are described by the followingmembership functions μ_(a)(dA_(n)) and μ_(r)(dA_(n)) defined over the dAuniverse [−1, 1]. FIG. 3 shows the relationship between the degrees ofmembership for those two categories over the universe [−1, 1] of allpossible combinations A_(th) and A_(pp).

The membership functions in FIG. 3 can be assigned to any event that isrepresented by a specific dA_(k) using a two dimensional vector,L_(n)=[μ_(a)(dA_(n)), μ_(r)(dA_(n))]′, representing its degrees ofmembership to each of the Accept/Reject categories. For example,dA_(n)=0.2 will translate to the degrees of membership to the Accept andReject categories: μ_(a)(0.2)=0; μ_(r)(0.2)=1.

The vector of membership values L₀(n) makes the association between asingle driving event and the possible driver characterization withrespect to that event. In order to characterize the long term behaviorof the driver we need an interpretation using probabilities that aregenerated by multiple events. By adding the membership values for eachevent, an aggregation of the overall, possibilities is made whereby thedriving style can be categorized as acceptance/rejection:

$\begin{matrix}{L = {\sum\limits_{n = 1}^{N}\lbrack {{\mu_{a}( {dA}_{n} )},{\mu_{r}( {dA}_{n} )}} \rbrack^{\prime}}} & (5)\end{matrix}$

where N is the total number of samples. The aggregated possibilities canbe considered as frequencies (sometimes referred to as fuzzyfrequencies) since they reveal how frequently and to what degree thedriver's actions can be cascaded to the two categories.

The alternative to aggregating the possibilities, i.e. adding themembership functions, is to add 1 if the specific membership grade isgreater than a prescribed threshold value, e.g. 0.9, or 0 otherwise,resulting in calculating the conventional frequencies of the categories.

From the aggregated possibilities we can calculate the probabilities ofthe acceptance/rejection categories: p_(i)=L_(i)/(L₁+L₂); i=1, 2.

The probabilities p_(i)'s are calculated from the aggregatedpossibilities (fuzzy frequencies) and can be considered as the “fuzzy”probabilities. The reason for the fuzziness here is the lack ofcertainty in characterizing the relationship between the two categories.For the special case of crisply defined categories (represented byintervals rather than fuzzy subsets) the possibilities transform toBoolean values, and their aggregated values become frequencies.Consequently the “fuzzy probabilities” p_(i)'s are translated to theconventional probabilities.

The frequencies based calculation of the probabilities p_(i)'s can beformally expressed in terms of the average frequenciesp_(i)=(L_(i)/N)/(L₁/N+L₂/N) i=1, 2.

Alternatively, the average frequencies can be replaced by theirexponentially weighted average counterparts where the higher weights areassigned to the possibilities that are associated with the most recentevents. Numerically, the process of generating a weighted average withhigher weights corresponding to the recent observation can beaccomplished by applying a low pass filter implementing the exponentialsmoothing algorithm in the time domain as follows:

L(n)=FL(n−1)+(1−F)□L ₀(n),  (6)

where the constant forgetting factor, 0<F≦1, controls the rate ofupdating the mean L* by assigning a set of exponentially decreasingweights to the older observations. For a constant forgetting factor(F□), a vector of positive weights (W) with unit sum is obtained:

W=[F ^(n)(1−F)F ^(n-1)(1−F)F ^(n-2) . . . (1−F)]  (7)

Vector W delineates a weighted average type aggregating operator withexponentially decreasing weights that are parameterized by theforgetting factor F. Parameter a defines the memory depth (the length ofthe moving window) of the weighted averaging aggregating operator. Itcan be shown that the memory depth K_(F□) is approximately reciprocal tothe forgetting factor, i.e. K_(F)=1/(1−F). Therefore, the low passfiltered value L*(n) of the membership grade vector represents theweighted averages of the individual possibilities over the weights W.Since all of the aggregated possibilities are calculated over the samemoving window of a length of K_(F)=1/(1−F), we can consider them asrepresentations of the frequencies of the associations with each of thetwo categories. Weighted average is calculated over the events withindexes belonging to a soft interval: sε{n−K_(F)+1, n]; where symbol{indicates a soft lower bound that includes values with lower indexesthan (n−K_(F)) with relatively low contribution. Consequently, theaggregated possibilities that form the vector L can be converted toprobabilities.

By manipulating the forgetting factor or making it dependent on certainconditions, we essentially change the moving window length and obtainlong or short term acceptance/rejection categories of the driver'spreferences.

Numerically, the process of characterization of the driver's style canbe significantly simplified if the fuzzy partitioning in FIG. 3 isreplaced by partitioning into two disjoint intervals. In this case it isenough to characterize only one of the categories. For instance, if therejection category is characterized we get:

L(n)=FL(n−1)+(1−F)□1 if A _(pp) >A _(th)  (8a)

L(n)=FL(n−1) otherwise  (8b)

For different values of the forgetting factor F we learn differentcharacterizations of the driver style. This simplification, however, hasan impact on the accuracy of the learned information about the driver;e.g., small and large values of dA=A_(th)−A_(pp) are interpretedidentically in (8a).

The simplified learning based characterization of the driving style isdefined by equations (8a) and (8b). Slow and fast directional forgettingfactors are used to learn both long term and short term driver behaviorsrespectively.

The framework of the learning control methodology is as follows:

M1) Determine Conditions for Using Learning Mechanism:

In this step, the learning mechanism is activated under acceptabledriving conditions, such as acceptable accelerator pedal position,acceptable vehicle speed, and acceptable acceleration.

M2) Determine the Long Term Characterization of the Driving Style L_(s).

In this step, a slow forgetting factor based learning of the rejectioncategory is determined as follows:

-   -   M2-a) When the accelerator pedal position is increasing or        ΔA_(pp) is positive, the slow forgetting factor, F_(s), is        determined as follows:

F _(s) =F _(sp),  (9)

-   -   where F_(sp) is the slow forgetting factor value when the        accelerator pedal position is increasing or ΔA_(pp) is positive,    -   M2-b) When the accelerator pedal position is decreasing or        ΔA_(pp) is negative, the slow forgetting factor, F_(s), is        determined as follows:

F _(s) =F _(sn),  (10)

-   -   where F_(sn) is the slow forgetting factor value when the        accelerator pedal position is decreasing or ΔA_(pp) is negative.    -   M2-c) When the accelerator pedal position, A_(pp), is less than        or equal to the A_(th), then the long term characterization of        the driving style L_(s), is determined as follows (4ii):

L _(s)(n)=F _(s) L _(s)(n−1)  (11)

-   -   M2-d) When the accelerator pedal position, A_(pp), is greater        than the A_(th), then the long term characterization of the        driving style L_(s), is determined as follows:

L _(s)(n)=F _(s) L _(s)(n−1)+(1−F _(s))  (12)

-   -   The long term characterization of the driving style L_(s) adapts        slowly to driver's style and summarizes its major preferences        over long period of time.

M3) Determine Short Term Characterization of the Driving Style.

There are situations even where the most fuel economy conscious driversmay want to sacrifice fuel economy improvements temporarily. Forexample, if a driver is merging or passing another vehicle, he or shemay want to temporarily sacrifice fuel economy improvement to gain extravehicle performance. Under such conditions, the driver may not want thefeedback (haptic or visual) to be intrusive. Hence, a short termcharacterization of the driving style is used and a fast forgettingfactor parameter that can be used to temporarily desensitize or reducethe system effectiveness for fuel economy improvement to achieve extra(more than usual desired by the driver) vehicle performance oracceleration. This short term characterization for temporary increasedperformance at the cost of reduced fuel efficiency is determined asfollows:

-   -   M3-a) When the accelerator pedal position, A_(pp), is greater        than the A_(th), the fast forgetting factor, F_(ƒ), is        determined as follows:

F _(ƒ) =F _(ƒp),  (13)

-   -   where F_(ƒp) is the fast forgetting factor value when the        accelerator pedal position, A_(pp), is greater than A_(th).    -   M3-b) When the accelerator pedal position, A_(pp), is less or        equal to the A_(th), the fast forgetting factor, F_(ƒ), is        determined as follows:

F _(ƒ) =F _(ƒnw),  (14)

-   -   where F_(ƒnw) is the fast forgetting factor value (to wait        before quickly forgetting the impact of the temporary condition)        when the accelerator pedal position, A_(pp), is less or equal        than A_(th).    -   M3-c) When the accelerator pedal position, A_(pp), is less or        equal to the A_(th), and the accelerator pedal position, A_(pp),        is greater than the A_(th), the fast forgetting factor, F_(ƒ),        is determined as follows:

F _(ƒ) =F _(ƒnq)  (15) (15)

-   -   where F_(ƒnq) is the fast forgetting factor value (which results        in quickly forgetting the impact of the temporary condition)        when the accelerator pedal position, A_(pp), is less or equal        than A_(th).    -   M3-d) When the accelerator pedal position, A_(pp), is less or        equal to the A_(th), then the short term characterization of the        driving style, L_(ƒ), is determined as follows:

L _(ƒ)(n)=F _(ƒ) L _(ƒ)(n−1).  (16)

-   -   M3-e) When the accelerator pedal position, A_(pp), is greater        than the A_(th), then the short term characterization of the        driving style, L_(ƒ), is determined as follows:

L _(ƒ)(n)=F _(ƒ) L _(ƒ)(n−1)+(1−F _(ƒ)).  (17)

-   -   The short term characterization of the driving style, L_(ƒ),        learns and adapts quickly to a driver's temporary intent to        change his or her driving style due to non-ordinary conditions        or desires, and tries to suppress the feedback for improving his        or her driving behavior under such conditions. Thereby, the        haptic or visual feedback will not be intrusive at all to the        driver's temporary intents to increase performance at the cost        of reducing fuel economy.

M4) Determine the Overall Aggregated Characterization of the DrivingStyle.

The final aggregated driver style characterization, is determined by theproduct type aggregation of the long and short term characterization asfollows:

L _(adpt)(n)=L _(ƒ)(n)L _(s)(n)  (18)

This aggregated category, L_(adpt), is adapted iteratively, and ismultiplied by the fuzzy input signal, the normalized fuel consumption(ƒc_(n)). Therefore, the input of the normalized fuel consumption to thefuzzy fuel consumption membership function in the multiple-input,multiple-output fuzzy logic based real-time advisory system is modifiedby this learned parameter, which adapts the behavior of the real-timeadvisory system according to the estimated driving style. In addition,it also modifies the behavior of the real-time advisory controller toaccommodate temporary (or short-term) conditions where the importance offuel economy improvement is superseded by the desire of increasedperformance.

In this way the multiple-input, multiple-output rule base with a fuzzyreasoning mechanism in the real-time advisory system, which decomposesthe space of the main factors that affect vehicle fuel economy andperformance (instantaneous fuel consumption, acceleration, speed, andaccelerator pedal position), can be learned or adapted to the specificdriver style and behavior.

The fuzzy advisory controller can effectively be tuned to providefeedback to the driver such that the fuel economy of the vehicle can beimproved in a real world driving environment. However, different typesof drivers may find or perceive the feedback differently. Even with thepotential for increasing fuel economy, this feedback or advice, whichresults in reduced performance can be unacceptable to some group ofdrivers. Similarly, another group of drivers may consider this feedbackto be too soft or below their expectations for their driving style andpreferences for fuel economy improvement, and hence may desire morefeedback or advice from the system to further improve their vehicle'sfuel economies. Alternatively, some group of drivers may consider thisfeedback to fit well to their driving style and preferences for fueleconomy versus desired performance. Finally, even for the same driver,the preference for fuel economy versus performance might be differentunder different circumstances. Hence, an adaptive algorithm that canlearn driver intentions by monitoring their driving style and behaviorcan be used to address these issues.

The adaptation algorithm uses the estimated driver characterization todynamically adapt the parameters of the fuzzy advisory controller to thespecific driver and improve its effectiveness. As explained previouslyin the preceding discussion, the adaptation algorithm continuallymonitors the reaction of the driver to the recommendation of theadvisory system (haptic or visual) and estimates driver preferences forperformance and fuel economy. The main idea behind the adaptationalgorithm is that if the feedback mechanism is not of the driver'sdesire, his or her accelerator response or behavior will indicate thisfact. Hence, the control information in the current driver response canbe used to learn a parameter that reflects the type of performance andfuel economy trade-off desired by the driver. In this way, the driver'sdesire for performance and fuel economy trade-off can be converged intoa learned factor over time, which would then be used for gain schedulingof the fuzzy advisory controller. In other words, the proposedadaptation algorithm continually learns the driver's acceptance orrejection of the advice issued by the fuzzy advisory controller andadapts it to the driver's desired behavior over time.

Since the rule based fuzzy algorithm provides advice that can either beaccepted or rejected by the driver, we can categorize the driving styleinto two categories for this algorithm. In the first category, theadvice from the controller is accepted which represents the desire fromthe driver to improve fuel economy. Therefore, this category can beassociated with fuel efficiency aware style of driving. The secondcategory is associated with a driver who rejects or ignores the advicefrom the advisory system. It can be noted here that the driving style,rather than the driver, is characterized because driving styleencompasses specific driver and vehicle environment goals such asdriving conditions, load, driver's cognitive/emotional state, etc.,which may cause a certain driver to exhibit different driving stylesunder different circumstances.

The acceptance or rejection of the advisory system recommendations canbe quantified through their frequencies. Since the acceptance/rejectionevents are complementary, it is enough to calculate the frequency ofoccurrence of one of them, e.g. the rejection events. Numerically, theprocess of recursive calculation of the weighted frequency of rejection(with higher weights corresponding to the recent observations) can beaccomplished by applying a low pass filter implementing the exponentialsmoothing algorithm:

L(n)=øL(n−1)+(1−ø)1 if A _(pp) >A _(th)  (19)

L(n)=øL(n−1) otherwise  (20)

where the constant forgetting factor, 0<ø<1, controls the rate ofupdating the weighted mean L of the events of rejecting the systemadvice, i.e. A_(pp)>A_(th). For a constant forgetting factor ø, weobtain a vector of positive weights can be obtained with unit sum asfollows:

W=[ø ^(n)(1−ø)ø^(n-1)(1−ø)ø^(n-2) . . . (1−ø)]  (21)

The vector W delineates a weighted average type aggregating operatorwith exponentially decreasing weights that are parameterized by theforgetting factor o. Parameter o defines the memory depth (the length ofthe moving window) of the weighted averaging aggregating operator. Itcan be shown that the memory depth K_(ø) is approximately a reciprocalof the forgetting factor, i.e. K_(ø)=1/(1−ø). Weighted average iscalculated over the events with indexes belonging to the soft interval:s

{n−K_(ø)+1,n]; where symbol {indicates a soft lower bound that includesvalues with lower indexes than (n−K_(ø)) with relatively lowcontribution.

By manipulating the forgetting factor or making it dependent on certainconditions, the moving window length is essentially changed, and a longor short term rejection categorization of the driver's preferences isobtained.

For different values of the forgetting factor (o), differentcharacterizations (long term or short term) of the driver style arelearned. This simplification, however, has an impact on the accuracy ofthe learned information about the driver; e.g., small and large valuesof dA=A_(th)−A_(pp) are interpreted identically in (19) and (20).

Incorporating slow and fast forgetting factors in equations (19) and(20) can be used to learn both long term and short term driverbehaviors, respectively. Slow forgetting factor (ø_(s)=0.95) is used toprovide a long term characterization of the driving style over a longertime (21):

L _(s)(n)=ø_(s) L(n−1)+(1−(1−ø_(s))1 if A _(pp) >A _(th)  (22)

L _(s)(n)=ø_(s) L(n−1) otherwise  (23)

There are situations even where the most fuel economy conscious driversmay want to sacrifice or disregard importance of fuel economyimprovements temporarily. For example, if a driver is merging or passinganother vehicle, he or she may want to temporarily sacrifice fueleconomy improvement to gain extra vehicle performance. Under suchconditions, the driver may not want the feedback (haptic or visual) tobe intrusive. Hence, a short term characterization of the driving styleand a fast forgetting factor are used:

L _(ƒ)(n)=ø_(ƒ) L(n−1)+(1−ø_(ƒ))1 if A _(pp) >A _(th)  (24)

L _(ƒ)(n)=ø_(ƒ) L(n−1) otherwise,  (25)

where ø_(ƒ)is the fast forgetting factor value (ø_(s)=0.8) to provide analternative measure of the instantaneous preferences of the driver. Theoverall aggregated driver style characterization is determined by theproduct type aggregation of the long and short term characterization asfollows:

L _(adpt)(n)=L _(ƒ)(n)L _(s)(n)  (26)

This aggregated characterization value, L_(adpt), is used for gainscheduling of the advisory controller. The gain scheduling is performedby multiplying (scaling) the input x₄, the normalized fuel consumption(ƒc_(n)), by the current aggregated characterization value L_(adpt). Theimpact of this dynamic rescaling of the normalized fuel consumptionƒc_(n) is the adaptation of the membership function value F_(4,j) ₄(L_(adapt)x₄) in expression (15) to the rate of rejection of the systemrecommended accelerated pedal position threshold A_(th); i.e., theestimated driving style. Consequently, this results in the adaptation ofthe aggregated membership value from the antecedents of the rules andthe output inferred by the fuzzy controller as follows:

$\begin{matrix}{y = \frac{\begin{matrix}{\sum\limits_{j}{{F_{1,j_{1}}( x_{1} )}{F_{2,j_{2}}( x_{2} )}{F_{3,j_{3}}( x_{3} )}{F_{4,j_{4}}( {L_{adapt}x_{4}} )}}} \\\lbrack {h_{1,k_{1}}h_{2,k_{2}}h_{3,k_{3}}} \rbrack\end{matrix}}{\sum\limits_{j}{{F_{1,j_{1}}( x_{2} )}{F_{2,j_{2}}( x_{2} )}{F_{3,j_{3}}( x_{3} )}{F_{4,j_{4}}( {L_{adapt}x_{4}} )}}}} & (27)\end{matrix}$

It is clear that this learned parameter modifies the behavior of thereal-time advisory controller to accommodate temporary (or short term)conditions where the importance of fuel economy improvement issuperseded by the desire of increased performance.

FIG. 4 shows the adaptive algorithm at 40 that is used to convert adriver's behavior effect to provide an input to the fuzzy advisorycontroller. Other components of the diagram of FIG. 4 carry the samenumerals used in FIG. 2 for corresponding components, although primenotations are added to the numerals in FIG. 4.

In the design of FIG. 4, the fuzzy rule-based driver advisorycontroller, input variables, output variables, and the associated fuzzyrule sets are defined. Two of the inputs are fuel economy error and rateof change of fuel economy error. In addition to these inputs, a thirdinput is engine power. These inputs only convey the effect on overallfuel economy in an HEV without taking into account drivability andintrusiveness for the driver. Also, these inputs can only provide anonlinear fuzzy logic based control that is completely feedback-basedand cannot take predictability nor opportunistic conditions into accountto improve fuel economy without affecting the drivability.

To take drivability into account, the essence of prediction and anability to take advantage of opportunistic states is important. One ofthe inputs, shown at 52, used in the diagram of FIG. 2 is fuel economy,which is calculated as a fraction of vehicle speed to fuel consumption.Since vehicle speed is a good indicator of vehicle driving state (citytype or highway driving), it is more relevant to use fuel consumptionand vehicle speed as inputs instead of just fuel economy. Hence, the twonew inputs that are used to achieve improved fuel economy anddrivability were the normalized fuel consumption (ƒc_(n)) and vehiclespeed (vs). The normalized fuel consumption is the ratio between theinstantaneous or actual fuel consumption and the maximum fuelconsumption during a given driving condition.

To address the criteria for acceptable drivability or performance, thevehicle should be able to achieve minimum acceptable acceleration at alltimes. Hence, one of the other inputs that is needed is the normalizedvehicle acceleration (a_(n)), the ratio between the instantaneous oractual vehicle acceleration and the maximum possible vehicleacceleration during a given driving condition.

To predict driver behavior and to make use of opportunistic states ofthe driving behavior, the driver pedal response (ζ_(ΔA)), which is thedifference between the actual pedal position and appropriately filteredpedal position, was also selected as an input as shown at 52.

One of the outputs of the improved controller is the advised change(delta) at 53 of the accelerator pedal position (ΔA_(u) _(—) _(lim))together with two other outputs, the maximum integrator offset 50(I_(oft) _(—) _(max)) and the minimum integrator offset 55 (I_(oft) _(—)_(min)) resulting in a multi-input multi-output (MIMO) advanced fuzzycontroller. The advised change of accelerator pedal position from thiscontroller is fed into an integrator 42 whose lower and upper bounds arecalculated based on actual pedal position 48 and minimum integraloffset, and actual pedal position and maximum integral offset 50, asshown in FIG. 3. This advanced fuzzy controller of FIG. 3 utilizes thehuman control knowledge and experience from the usage and testing offuzzy advisory controller to intuitively construct a more sophisticatedintelligent controller so that the resulting controller will emulate thedesired control behavior to a certain extent. FIG. 2 shows the fuzzyadvisory controller at 46.

The design characteristics of the MIMO (multiple input/multiple output)fuzzy rule-based driver advisory controller is as follows: 1) the inputvariables consist of driver accelerator pedal response ((ζ_(ΔA)),vehicle speed (vs), normalized vehicle acceleration (a_(n)), andnormalized fuel consumption (ƒc_(n)); 2) the output variables consist ofadvised change of the pedal position (ΔA_(u) _(—) _(lim)), maximumintegrator offset (I_(oft) _(—) _(max)), and minimum integrator offset(I_(oft) _(—) _(min)) 3) the input fuzzy sets or membership functionsare chosen to be low and high trapezoidal functions, where b_(1L), b,b_(3L), b_(4L) represents the low value of the trapezoidal functions;and 4) the three output fuzzy sets are of singleton type for the advisedchange of (delta) accelerator pedal position, maximum integrator offset,and minimum integrator offset. The output fuzzy sets for maximumintegrator offset are of singleton type representing high (h_(2H)), low(h_(2L)) and zero (h_(2z)) values. Similarly, the output fuzzy sets forminimum integrator offset are of singleton type representing negativehigh (−High or −h_(3H)), negative low (−Low or −h_(3H)) and zero(h_(3z)) values. Finally, the output fuzzy sets for advised change ofaccelerator pedal position is of singleton type representing high(h_(1H)), low (h_(1L)) and negative high (−High or −h_(1H)) values. Thefuzzy rules for this controller are described above in Table 1.

The rules in Table 1 exemplify different HEV conditions, such as steadystate and transient, together with feedback-based corrective,opportunistic and predictive conditions. These conditions are defined bythe rule antecedents and the corresponding recommended changes of theupper limit of the accelerator pedal as consequents. These rules arelaid out so that they describe and address various different drivingconditions where fuel efficiency can be improved and acceptable vehicleperformance can be achieved.

As mentioned above, the fuzzy rules are laid out in a manner such thatthey can distinguish between various HEV driving behaviors and makeopportunistic, predictive or corrective decisions. Among these fuzzyrules, some of the rules are intended to cover steady state and otherswill cover transient conditions. In addition, these fuzzy rules coverconditions where a fast or slow corrective action is required to improvefuel economy of the vehicle. Also some of the other fuzzy rules providethe ability for the controller to anticipate conditions where thecontroller may detect a condition of inefficient fuel consumption due tosome environmental factor and will set its outputs appropriately to lookfor opportunities to indicate driver to improve driving behavior. Inaddition, some of the other rules look at the current conditions wherecurrent fuel consumption is low, but due to driver behavior they canpredict that fuel economy will degrade in near future and hence can takepredictive actions to provide a mechanism to avoid possible undesirablebehaviors.

For example, Rule 8, where x_(l)(n) is low, but x₂(n), x₃(n) and x₄(n)are all high, depicts an almost steady state condition where vehicleacceleration and fuel consumption is high, so there is a need to takefast corrective action by indicating to driver that he/she needs to slowdown by scheduling a negative high value (−_(1H)) for the first output(y₁(n) or ΔA_(u) _(—) _(lim)), zero (h_(2Z)) for the second output(y₂(n) or I_(oft) _(—) _(max)) and negative high (−h_(3H)) for the thirdoutput (y₃(n) or I_(oft) _(—) _(min)).

Rule 6, where x₁(n) is low, x₂(n) is high, and x₃(n) is low and x₄(n) ishigh indicates an opportunistic condition where the system is in asteady state condition and fuel consumption is high, so the controllerneeds to indicate that driving style is fine but at the same time itneeds to prepare to look for opportunity to improve fuel consumption byscheduling a low value (h_(1L)) for the first output (y₁(n) or ΔA_(u)_(—) _(lim)), low value (h_(2L)) for the second output (y₂(n) or l_(oft)_(—) _(max)) and zero (h_(3Z)) for the third output (y₃(n) or I_(oft)_(—) _(min)).

Rule 9, where x₁(n) is high, but x₂(n), x₃(n) and x₄(n) are all low,indicates a predictive condition where current fuel consumption andacceleration conditions are fine, but due to the faster driver response,the controller needs to take predictive actions to prepare for anyupcoming inefficiency in fuel consumption by scheduling a negative lowvalue (−h_(1L)) for the first output (y₁(n) or ΔA_(u) _(—) _(lim)), highvalue (h_(2H)) for the second output (y₂(n) or I_(oft) _(—) _(max)) andzero (h_(3z)) for the third output (y₃(n) or I_(oft) _(—) _(min)).

In summary, these rules provide a method to schedule appropriate outputsfor the advanced MIMO fuzzy advisory controller according to thepowertrain conditions.

Although a particular embodiment of the invention is disclosed, a personskilled in the art may make modifications without departing from theinvention. All such modifications are intended to be within the scope ofthe following claims. While exemplary embodiments are described above,it is not intended that these embodiments describe all possible forms ofthe invention. Rather, the words used in the specification are words ofdescription rather than limitation, and it is understood that variouschanges may be made without departing from the spirit and scope of theinvention. Additionally, the features of various implementingembodiments may be combined to form further embodiments of theinvention.

1-11. (canceled)
 12. A method for controlling a hybrid electric vehiclehaving a mechanical power source and an electro-mechanical power sourceand a controller with an adaptive algorithm that learns a vehicledriver's driving preference, comprising; monitoring a driver's drivingstyle during a defined term of a given driving event characterized bymultiple vehicle operating conditions; and providing advisory feedbackinformation to change driver behavior for a given vehicle operatingcondition based on driving preference of the driver.
 13. The method ofclaim 12 wherein the advisory feedback information comprises an advisoryaccelerator pedal position.
 14. The method of claim 12 wherein theadaptive algorithm dynamically adapts parameters of fuzzy logic rules toeach of multiple sets of vehicle driving conditions and wherein theadvisory feedback information includes driver advice for adjustingaccelerator pedal position to conform driving style to current drivingconditions.
 15. The method of claim 12 wherein the adaptive algorithmmonitors a reaction of the driver to the advisory feedback informationto determine frequency of acceptance and rejection of the advisoryfeedback information associated with a long term driving style and ashort term driving style.
 16. The method of claim 12 wherein thecontroller is configured to calculate a forgetting factor value, thecontroller adjusting the forgetting factor to learn either a long termdriving style or a short term driving style.
 17. The method of claim 16wherein a fast forgetting factor is associated with a short term drivingstyle.
 18. The method of claim 12 further comprising: providing advisoryfeedback information based on fuzzy rules with inputs based on fueleconomy error between actual fuel economy and optimal fuel economy, andrate of change of the fuel economy error.
 19. The method of claim 18wherein the fuzzy rules include inputs based on input power from themechanical power source.
 20. The method of claim 18 wherein the fuzzyrules includes a first set of fuzzy rules governing steady state vehicleoperating conditions and a second set of fuzzy rules governing transientoperating conditions.
 21. An adaptive driver advisory control system fora hybrid vehicle, comprising: an accelerator pedal; a controller incommunication with the accelerator pedal and having a logic-basedalgorithm for learning a driver's driving style and being calibratedwith powertrain operating data corresponding to a desired fuel economy,the controller configured to monitor driver power demands to determinethe driving style and provide advisory information based on previousacceptance or rejection of the advisory information.
 22. The system ofclaim 21 wherein the controller is configured to normalize a variableindication of driver accelerator pedal response to changes in vehicledriving conditions and to normalize vehicle operating variables; andwherein the controller is configured to develop output signals based onthe operating variables indicating an advised accelerator pedalposition.